Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental healthDownload the open access paper
This paper presents an example of the use of a FUPS dataset in the complex system of child mental healthcare. The paper explores the use of this FUPS dataset to support meaningful dialogue between key stakeholders, including service providers, funders and users, in relation to outcomes of services. The term ‘FUPS’ is proposed to describe these flawed, uncertain, proximate and sparse datasets. Authors: Wolpert, M., Rutter, H. (2018).
Prevalence of mental health problems in schools: poverty and other risk factors amongst 28,000 adolescents in EnglandDownload the open access paper
This study analyses a large-scale community-based dataset of 28 160 adolescents to explore school-based prevalence of mental health problems and characteristics that predict increased odds of experiencing them. Authors: Deighton, J., Lereya, T.L., Casey, P., Patalay, P., Humphrey, N., & Wolpert, M. (2019).
An overview of developmental behavioral geneticsRead the abstract
In this chapter, we present an overview of the field of developmental behavioural genetics, which serves as important context for understanding the field of behavioral epigenetics. Authors: Austerberry, C., Fearon, P. (2020).
The therapeutic process in psychodynamic therapy with children with different capacities for mentalizingRead the abstract
The aim of this study was to explore the therapeutic process in psychodynamic therapy with school-age children with different kinds of difficulties and mentalizing profiles. Authors: Ramires, V., Carvalho, C., Goodman, G., Midgley, N. & Polli. R. (2020).
How does the association between special education need and absence vary overtime and across special education need types?Download the open access paper
We investigated special education needs (SEN) as a risk factor for absenteeism. For 418,455 mainstream secondary school students from 151 local authorities in England, multilevel linear regression models were run to investigate the association between SEN, SEN types and absenteeism during their secondary school period from year 7 to year 11. Authors: Lereya, T., Cattan, S, Yoon, Y., Gilbert, R. & Deighton, J. (2022).